Exam: 1Z0-1110-22
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Format: Multiple Choice
Duration: 90 Minutes
Number of Questions: 55
Passing Score: 65%
Validation: This exam has been validated against Oracle Cloud Infrastructure
2022
Earn associated certifications
Passing this exam is required to earn these certifications. Select each
certification title below to view full requirements.
Oracle Cloud Infrastructure Data Science 2022 Certified Professional
Prepare to pass exam: 1Z0-1110-22
The Oracle Cloud Infrastructure Data Science Certified Professional
certification is intended for data scientists and other professionals
responsible for building data science solutions and managing the complete
lifecycle of machine learning models. The certification validates a candidate’s
ability to effectively identify OCI services used for implementing machine
learning solutions for specific business use cases. It also validates a
candidate’s ability to incorporate machine learning best practices.
Take recommended training
Complete one of the courses below to prepare for your exam (optional):
Oracle Cloud Infrastructure Learning Subscription
Additional Preparation and Information
A combination of Oracle training and hands-on experience (attained via labs
and/or field experience), in the learning subscription, provides the best
preparation for passing the exam.
Review exam topics
Configure your tenancy for OCI Data Science
Discuss OCI Data Science Overview & Concepts
Discuss Accelerated Data Science (ADS) SDK Capabilities
Configure your tenancy for Data Science
Design and Set up OCI Data Science Workspace
Create and manage Projects and Notebook sessions
Create and manage Conda environments
Store credentials via OCI Vault
Configure and manage source code in Code Repositories (Git)
Implement end-to-end Machine Learning Lifecycle
Discuss ML Lifecycle Overview
Access data from different sources
Explore and Prepare data
Visualize and Profile data
Create and train models using OCI and Open source Libraries
Create and Use automated ML capability from Oracle AutoML
Evaluate models
Obtain Global & Local Model Explanations
Manage Models using Model Catalog
Deploy & Invoke a Cataloged Model
Apply MLOps Practices
Discuss general MLOps Architecture in OCI
Create & Manage Jobs for custom tasks
Scale with OCI Data Science
Monitor & Log using MLOps Practices
Use related OCI Services
Create and Manage Spark Applications using Data Flow and OCI Data Science
Explain core OCI Open Data Service concepts
Create and Export a Dataset using OCI Data Labeling
Use OCI AI Services for ML Solutions
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QUESTION 1
You are a data scientist with a set of text and image files that need
annotation, and you want to use
Oracle Cloud Infrastructure (OCI) Data Labeling. Which of the following THREE
an-notation classes are supported by the tool.?
A. Object Detection
B. Named Entity Extraction
C. Classification (single/multi label)
D. Key-Point and Landmark
E. Polygonal Segmentation
F. Semantic Segmentation
Answer: A, B, C
QUESTION 2
What preparation steps are required to access an Oracle AI service SDK from
a Data Science notebook session?
A. Call the Accented Data Science (ADS) command to enable Al integration
B. Create and upload the API signing key and config file
C. Import the REST API
D. Create and upload execute.py and runtime.yaml
Answer: B
QUESTION 3
Which feature of the Oracle Cloud Infrastructure (OCI) Vision service helps
you generate in-dexing tags for a collection of marketing photographs?
A. Document classification
B. Image classification
C. Text recognition
D. Key Value extraction
Answer: C
QUESTION 4
You are preparing a configuration object necessary to create a Data Flow
application.
Which THREE parameter values should you provide?
A. The path to the arhive.zip file.
B. The local path to your pySpark script.
C. The compartment of the Data Flow application.
D. The bucket used to read/write the pySpark script in Object Storage.
E. The display name of the application.
Answer: B, D, E
QUESTION 5
You have just received a new data set from a colleague. You want to quickly find
out summary
information about the data set, such as the types of features, total number of
observations, and data distributions,
Which Accelerated Data Science (ADS) SDK method from the AD&Dataset class would
you use?
A. Show_in_notebook{}
B. To_xgb{}
C. Compute{}
D. Show_corr{}
Answer: A
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